Learning-based domain transformation for medical images

ABSTRACT

Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

TECHNICAL FIELD

The subject disclosure relates generally to medical images, and morespecifically to learning-based domain transformation for medical images.

BACKGROUND

A medical imaging device can capture a medical image of an anatomicalstructure of a patient for purposes of diagnosis and/or prognosis. Thereexist various medical scanning domains in which and/or by which themedical imaging device can capture the medical image. Different medicalscanning domains can be implemented for different purposes and/or indifferent contexts. Although it may be desirable in some cases tocapture the medical image in a particular medical scanning domain, itcan sometimes be the case that the particular medical scanning domain isunavailable and that the medical image can be captured only in adifferent medical scanning domain. Unfortunately, there do not exist anysystems and/or techniques that can address this problem.

Accordingly, systems and/or techniques that can address one or more ofthese technical problems can be desirable.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, devices, systems, computer-implemented methods,apparatus and/or computer program products that facilitatelearning-based domain transformation for medical images are described.

According to one or more embodiments, a system is provided. The systemcan comprise a computer-readable memory that can storecomputer-executable components. The system can further comprise aprocessor that can be operably coupled to the computer-readable memoryand that can execute the computer-executable components stored in thecomputer-readable memory. In various embodiments, thecomputer-executable components can comprise a receiver component. Invarious cases, the receiver component can access a medical image. Invarious instances, the medical image can depict an anatomical structureaccording to a first medical scanning domain. In various aspects, thecomputer-executable components can further comprise a transformationcomponent. In various cases, the transformation component can generate,via execution of a machine learning model, a predicted image based onthe medical image. In various instances, the predicted image can depictthe anatomical structure according to a second medical scanning domainthat is different from the first medical scanning domain.

According to one or more embodiments, the above-described system can beimplemented as a computer-implemented method and/or a computer programproduct.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat facilitates learning-based domain transformation for medical imagesin accordance with one or more embodiments described herein.

FIG. 2 illustrates a block diagram of an example, non-limiting systemincluding a machine learning model that facilitates learning-baseddomain transformation for medical images in accordance with one or moreembodiments described herein.

FIG. 3 illustrates an example, non-limiting block diagram showing how amachine learning model as described herein can transform the domain of amedical image in accordance with one or more embodiments describedherein.

FIG. 4 illustrates a block diagram of an example, non-limiting systemincluding a segmentation model that facilitates learning-based domaintransformation for medical images in accordance with one or moreembodiments described herein.

FIG. 5 illustrates an example, non-limiting block diagram showing how asegmentation model and a machine learning model as described herein cantransform the domain of a medical image in accordance with one or moreembodiments described herein.

FIG. 6 illustrates a block diagram of an example, non-limiting systemincluding a denoise component that facilitates learning-based domaintransformation for medical images in accordance with one or moreembodiments described herein.

FIG. 7 illustrates a flow diagram of an example, non-limitingcomputer-implemented method that facilitates learning-based domaintransformation for medical images in accordance with one or moreembodiments described herein.

FIG. 8 illustrates a block diagram of an example, non-limiting systemincluding a training component that facilitates learning-based domaintransformation for medical images in accordance with one or moreembodiments described herein.

FIG. 9 illustrates a flow diagram of an example, non-limitingcomputer-implemented method that facilitates training of a domaintransformation machine learning model in accordance with one or moreembodiments described herein.

FIG. 10 illustrates a block diagram of an example, non-limiting systemincluding an execution component that facilitates learning-based domaintransformation for medical images in accordance with one or moreembodiments described herein.

FIG. 11 illustrates a flow diagram of an example, non-limitingcomputer-implemented method that facilitates learning-based domaintransformation for medical images in accordance with one or moreembodiments described herein.

FIG. 12 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

FIG. 13 illustrates an example networking environment operable toexecute various implementations described herein.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

A medical imaging device (e.g., a computed tomography (CT) scanner, amagnetic resonance imaging (MRI) scanner, an X-ray scanner, anultrasound scanner, a positron emission tomography (PET) scanner) cancapture and/or generate a medical image (e.g., a CT image, an MRI image,an X-ray image, an ultrasound image, a PET image) of an anatomicalstructure of a patient for purposes of diagnosis and/or prognosis.

There exist various medical scanning domains in which and/or by whichthe medical imaging device can capture/generate the medical image. Asthose having ordinary skill in the art will appreciate, a medicalscanning domain can be any suitable configurable setting, configurablecontrol, and/or configurable parameter of the medical imaging devicethat can influence and/or otherwise affect how the medical image willvisually appear once captured/generated. As a non-limiting example, amedical scanning domain can be a particular electrical energy level(e.g., measured in peak kilovoltage (kVp), measured in kilo electronvolts (keV), and/or measured in milliamps (mA)) that is utilized by themedical imaging device to capture/generate the medical image. As thosehaving ordinary skill in the art will appreciate, the medical image canexhibit various visual characteristics (e.g., a particular Hounsfieldunit (HU) intensity distribution) if captured/generated via a lowelectrical energy level, and the medical image can exhibit differentvisual characteristics if captured/generated via a high electricalenergy level. As another example, a medical scanning domain can be aparticular contrast phase (e.g., non-contrast, early arterial phase,late arterial phase, hepatic phase, nephrogenic phase, venous phase,delayed phase) that is utilized by the medical imaging device tocapture/generate the medical image. As above, the medical image canexhibit various visual characteristics if captured/generated using agiven contrast phase, and the medical image can exhibit different visualcharacteristics if captured/generated using a different contrast phase.

Different medical scanning domains can be implemented for differentpurposes and/or in different contexts. For instance, there exist variouscomputerized diagnostic techniques (e.g., tumor detection algorithms,calcium scoring algorithms, occlusion detection algorithms) that canautomatically analyze the medical image so as to generate a diagnosisand/or prognosis for the patient. A particular computerized diagnostictechnique can function by assuming that the medical image wascaptured/generated via a particular medical scanning domain. So, if theassumed medical scanning domain does not match the actual medicalscanning domain that was used by the medical imaging device tocapture/generate the medical image, the particular computerizeddiagnostic technique can yield inaccurate diagnosis/prognosis results.

Although it may be desirable in some cases to capture/generate themedical image via a certain medical scanning domain, it can sometimes bethe case that the certain medical scanning domain is unavailable andthat the medical image can be captured/generated only in a differentmedical scanning domain. For example, consider Agatston scoring, whichis a particular automated technique for facilitating calcium scoring ofCT images. In various aspects, it can be desired to apply Agatstonscoring to a CT image of a patient, so that a calcium score can begenerated for the patient. Note that Agatston scoring is defined for CTimages that are captured/generated at 120 kVp. Thus, if the CT image ofthe patient is captured/generated at some energy level other than 120kVp, Agatston scoring cannot be reliably applied to the CT image.However, 120 kVp can be considered as a rather high dose in currentclinical practice, and capturing/generating the CT image at an energylevel of 120 kVp can expose the patient to heightened levels ofradiation which can increase the patient’s risk of developing cancer. Inthis scenario, the patient’s immediate health can be best served bycapturing/generating a CT image at a lower energy level (e.g., 70 kVp),but such a CT image can exhibit increased levels of noise, lower visualquality, and/or other loss of detail and cannot then be accuratelyanalyzed by Agatston scoring. This non-limiting example helps toillustrate how, in current clinical practice, some considerations (e.g.,underlying assumptions of a computerized diagnostic technique that isdesired to be applied to a medical image) might tend to favor utilizingone medical scanning domain (e.g., 120 kVp), while other countervailingconsiderations (e.g., desire to not expose patients to heightenedradiation levels) might tend to favor utilizing a different medicalscanning domain (e.g., 70 kVp).

Unfortunately, there do not exist any systems and/or techniques that canaddress this problem. To continue the above example, when existingtechniques are implemented, either the CT image of the patient iscaptured/generated at 120 kVp, in which case the patient is exposed todangerous levels of radiation, or the CT image of the patient iscaptured/generated at a lower energy level, in which case Agatstonscoring cannot be accurately applied to the CT image. In other words,existing techniques cannot prevent the patient from being exposed toheightened radiation levels while also allowing the resulting CT imageto be accurately analyzed by Agatston scoring.

Accordingly, systems and/or techniques that can address one or more ofthese technical problems can be desirable.

Various embodiments of the subject innovation can address one or more ofthese technical problems. One or more embodiments described hereininclude systems, computer-implemented methods, apparatus, and/orcomputer program products that can facilitate learning-based domaintransformation for medical images. As mentioned above, it can often bethe case that a particular medical scanning domain is preferred and/ordesired to capture/generate a medical image, but despite such preferenceand/or desire, it can be the case that only a different medical scanningdomain is available. As a solution to this technical problem, theinventors of various embodiments of the subject innovation devisedlearning-based domain transformation for medical images. In variousaspects, learning-based domain transformation for medical images caninvolve training a machine learning model to receive as input a medicalimage that was captured/generated via a first medical scanning domainand to produce as output a version of the medical image according to asecond medical scanning domain. In other words, the machine learningmodel can be configured to predict what the medical image would looklike if it had been originally captured/generated via the second medicalscanning domain. Accordingly, computerized diagnostic techniques thatassume that the medical image was captured/generated via the secondmedical scanning domain can be applied to the predicted output yieldedby the machine learning model, notwithstanding that the medical imagewas actually captured/generated via the first medical scanning domain.

In various instances, embodiments of the subject innovation can beconsidered as a computerized tool that can facilitate learning-baseddomain transformation for medical images. In various cases, thecomputerized tool described herein can comprise a receiver componentand/or a transformation component.

In various embodiments, there can be a medical image that depicts ananatomical structure of a patient according to a first medical scanningdomain. In various aspects, the anatomical structure can be any suitablebody part and/or portion thereof of the patient, such as a brain, aneye, a lung, a heart, a kidney, an intestine, a vein, an artery, a bone,a chest, and/or an abdomen. In various instances, the medical image canbe any suitable type of medical image, such as a CT image, an MRI image,an X-ray image, an ultrasound image, and/or a PET image. Moreover, invarious cases, the medical image can have any suitable format and/ordimensionality. For ease of explanation, this disclosure will mainlydescribe the medical image as being a two-dimensional array of pixels.However, those having ordinary skill in the art will appreciate thatvarious embodiments described herein are equally applicable when themedical image is a three-dimensional array of voxels (e.g., across-sectional slice of a three-dimensional array of voxels can itselfbe a two-dimensional array of pixels).

In various instances, the first medical scanning domain can be anysuitable medical scanning domain as desired, such as a first electricalenergy level and/or a first contrast phase. In various aspects, it canbe desired to apply a computerized diagnostic technique to the medicalimage, where the computerized diagnostic technique assumes that themedical image was captured/generated via a second medical scanningdomain that is different from the first medical scanning domain. Forexample, if the first medical scanning domain is a first electricalenergy level, the second medical scanning domain can be a secondelectrical energy level that is different from the first electricalenergy level. As another example, if the first medical scanning domainis a first contrast phase, the second medical scanning domain can be asecond contrast phase that is different from the first contrast phase.In various instances, the computerized tool described herein can beconfigured to translate and/or transform the first medical scanningdomain into the second medical scanning domain, such that the desiredcomputerized diagnostic technique can be applied.

In various embodiments, the receiver component of the computerized toolcan electronically receive and/or otherwise electronically access themedical image. In various aspects, the receiver component can retrievethe medical image from any suitable data structure (e.g., graph datastructure, relational data structure, hybrid data structure) that iselectronically accessible to the receiver component, whether the datastructure is centralized and/or decentralized, and/or whether the datastructure is remote from and/or local to the receiver component. Forexample, in some cases, the receiver component can retrieve the medicalimage from a medical imaging device (e.g., a CT scanner, an MRI scanner,an X-ray scanner, an ultrasound scanner, a PET scanner) that capturedand/or generated the medical image. In any case, the receiver componentcan obtain and/or access the medical image, such that other componentsof the computerized tool can electronically interact with (e.g., read,write, manipulate) the medical image.

In various embodiments, the transformation component of the computerizedtool can electronically generate, via execution and/or inferencing of amachine learning model, a predicted image based on the medical image. Invarious aspects, the machine learning model can be configured such thatthe predicted image depicts the anatomical structure according to thesecond medical scanning domain, instead of the first medical scanningdomain. In other words, the medical image can be actuallycaptured/generated via the first medical scanning domain, and themachine learning model can be configured to predict and/or infer whatthe medical image would look like if the medical image had been insteadcaptured/generated according to the second medical scanning domain.

In various instances, the machine learning model can exhibit anysuitable artificial intelligence architecture. As a non-limitingexample, the machine learning model can exhibit a deep learningregression architecture. That is, the machine learning model can haveany suitable number of neural network layers, can have any suitablenumbers of neurons in various layers (e.g., different layers can havedifferent numbers of neurons), can have any suitable types of activationfunctions in various neurons (e.g., softmax, sigmoid, hyperbolictangent, rectified linear unit), and/or can have any suitableinterneuron connectivity pattern (e.g., forward connections, skipconnections, recursive connections, recurrent connections). In any case,the machine learning model can exhibit any suitable artificialintelligence architecture such that the machine learning model canreceive as input the medical image, which is captured/generated in thefirst medical scanning domain, and can produce as output the predictedimage, which represents how the medical image would look in the secondmedical scanning domain.

In various alternative embodiments, the computerized tool can furthercomprise a segmentation component. In various aspects, prior toexecuting the machine learning model on the medical image, thesegmentation component can electronically identify, via execution and/orinferencing of a pre-trained segmentation model, a region-of-interest inthe medical image. In various instances, the region-of-interest can beany suitable portion of the medical image (e.g., any suitable subset ofpixels/voxels of the medical image) that is considered and/or deemed tobe of interest from a diagnostic and/or prognostic perspective. Forinstance, the region-of-interest can be considered as a particularportion of the medical image on which it is desired to apply thecomputerized diagnostic technique. For example, in some cases, theregion-of-interest can include the anatomical structure that is depictedin the medical image and can exclude background portions of the medicalimage. As another example, in some cases, the region-of-interest caninclude a specific portion of the anatomical structure and can excludeboth a remaining portion of the anatomical structure and backgroundportions of the medical image. In any case, once the segmentationcomponent has identified the region-of-interest, the segmentationcomponent can electronically crop out of the medical image any portionsof the medical image that are not within the region-of-interest. Asthose having ordinary skill in the art will appreciate, the pre-trainedsegmentation model can exhibit any suitable architecture as desired(e.g., can be a deep learning segmentation model) and can be trained viaany suitable learning paradigm (e.g., supervised training, unsupervisedtraining, reinforcement learning).

Although the segmentation component can be configured to identify theregion-of-interest and to crop out portions of the medical image thatare not within the region-of-interest, those having ordinary skill inthe art will appreciate that this is a mere non-limiting example. Invarious cases, the segmentation component can instead be configured toidentify one or more regions-of-disinterest in the medical image and tocrop out such one or more regions-of-disinterest. In any case, thesegmentation component can produce a cropped version of the medicalimage that includes one or more regions-of-interest and that excludesone or more regions-of-disinterest and/or one or more backgroundregions.

In various aspects, after the segmentation component has cropped out ofthe medical image any portions that are not within theregion-of-interest, the transformation component can execute the machinelearning model on the cropped medical image, thereby causing the machinelearning model to output a cropped predicted image. Because the croppedmedical image can depict only the region-of-interest according to thefirst medical scanning domain, the cropped predicted image cancorrespondingly depict only the region-of-interest according to thesecond medical scanning domain. In various aspects, by configuring themachine learning model to receive as input the cropped medical image(e.g., which contains fewer pixels/voxels than the full medical image)and to produce as output the cropped predicted image (e.g., whichcontains fewer pixels/voxels than the full predicted image), the machinelearning model can be considered as focusing on transforming the domainof the region-of-interest without getting bogged down and/or distractedby transforming the domain of the background portions of the medicalimage. In various cases, this can allow the machine learning model toachieve a higher level of performance accuracy than would otherwise bepossible.

In various alternative embodiments, the computerized tool can furthercomprise a denoise component. In various aspects, prior to the executionof the pre-trained segmentation model on the medical image, the denoisecomponent can electronically evaluate a noise level of the medicalimage. If the denoise component determines that the noise level does notexceed any suitable threshold value, the denoise component can refrainfrom taking further action. On the other hand, if the denoise componentdetermines that the noise level exceeds any suitable threshold value,the denoise component can electronically apply one or more denoisingtechniques to the medical image. In various instances, any suitabledenoising techniques can be implemented. For example, in some cases, thedenoise component can apply a denoise filter to the medical image,thereby reducing the noise level of the medical image. In other cases,the denoise component can execute a pre-trained denoising model on themedical image, which can be configured to output a denoised version ofthe medical image. As those having ordinary skill in the art willappreciate, the pre-trained denoise model can exhibit any suitableartificial intelligence architecture as desired (e.g., can be a deeplearning model) and can be trained via any suitable learning paradigm(e.g., supervised training, unsupervised training, reinforcementlearning).

In various aspects, after the denoise component has applied one or moredenoise techniques to the medical image, the segmentation component cancrop out background portions of the denoised medical image, and thetransformation component can execute the machine learning model on thedenoised and cropped medical image, thereby yielding a denoised andcropped predicted image. Those having ordinary skill in the art willappreciate that, in some cases, the order of application of denoisingand segmentation can be switched as desired. That is, in variousaspects, the segmentation component can be configured to crop out of themedical image any background portions, the denoise component can beconfigured to apply one or more denoising techniques to the croppedmedical image, and the transformation component can be configured toexecute the machine learning model on the cropped and denoised medicalimage. In any case, the performance of the machine learning model can befurther improved when the denoise component is implemented (e.g., themachine learning model can be not distracted by excessive noise in themedical image).

In various embodiments, the computerized tool can further comprise anexecution component. In various aspects, after the transformationcomponent has executed the machine learning model on the medical image(e.g., which can be cropped and/or denoised, as mentioned above) so asto yield the predicted image, the execution component can electronicallyapply the computerized diagnostic technique to the predicted image.Accordingly, the execution component can yield one or more diagnosticand/or prognostic results for the patient, and the execution componentcan electronically transmit such results to any suitable computingdevice as desired.

In order for the machine learning model to facilitate accuratetransformation of the first medical scanning domain to the secondmedical scanning domain, the machine learning model first requirestraining. Accordingly, in various embodiments, the computerized tool canfurther comprise a training component. In various aspects, prior toexecution and/or inferencing of the machine learning model, the receivercomponent can electronically access a training dataset, and the trainingcomponent can electronically train the machine learning model on thetraining dataset.

More specifically, the training dataset can include a set of trainingimages and a set of annotation images that respectively correspond tothe set of training images. That is, each training image in the set oftraining images can respectively correspond to a unique annotation imagein the set of annotation images. Consider a particular training imagethat corresponds to a particular annotation image. In various aspects,the particular training image can depict one or more anatomicalstructures according to the first medical scanning domain. In contrast,the particular annotation image can depict the same one or moreanatomical structures according to the second medical scanning domain.Moreover, in various instances, the particular training image can beregistered and/or aligned with the particular annotation image. Invarious aspects, such registration and/or alignment can be facilitatedin any suitable fashion. For example, in some cases, the particulartraining image can be co-registered with the particular annotation imagethrough simulation, through scanning physical phantoms, and/or throughdual-energy scans. As another example, in some cases, the particularannotation image can be fixed and the particular training image can beiteratively perturbed (e.g., iteratively rotated counter-clockwiseand/or clockwise, iteratively shifted up and/or down, iterativelyshifted left and/or right) until pixel-to-pixel (or voxel-to-voxel)registration is achieved between the particular training image and theparticular annotation image. As yet another example, in other cases, theparticular training image can be fixed and the particular annotationimage can be iteratively perturbed (e.g., iteratively rotatedcounter-clockwise and/or clockwise, iteratively shifted up and/or down,iteratively shifted left and/or right) until pixel-to-pixel (orvoxel-to-voxel) registration is achieved between the particular trainingimage and the particular annotation image. As yet another example, insome cases, a pre-trained registration model (e.g., deep learning model)can be executed on the particular training image and/or the particularannotation image, so as to ensure registration/alignment. In any case,the particular training image and the particular annotation image can beregistered and/or aligned, such that the one or more anatomicalstructures depicted in the particular training image have the samerespective coordinates as the one or more anatomical structures depictedin the particular annotation image.

In embodiments where the segmentation component and denoise componentare not implemented, the training component can train the machinelearning model on the training dataset as follows. In various instances,the internal parameters (e.g., weights, biases) of the machine learningmodel can be randomly initialized. In various cases, the trainingcomponent can select a training image from the training dataset. Invarious aspects, the training component can register/align the selectedtraining image with its corresponding annotation image, if suchregistration/alignment has not already been facilitated. In variousinstances, the training component can feed the selected training imageas input to the machine learning model. This can cause the machinelearning model to generate as output a predicted image, where thepredicted image represents how the selected training image would look ifthe selected training image were captured/generated via the secondmedical scanning domain rather than the first medical scanning domain.If the machine learning model has so far undergone no and/or littletraining, the predicted image can be rather inaccurate. In variousinstances, the training component can compute an error/loss between thepredicted image and the corresponding annotation image. Accordingly, thetraining component can update, via backpropagation, the internalparameters of the machine learning model based on the computederror/loss.

In various cases, the training component can repeat this procedure foreach training image in the training dataset, thereby causing theinternal parameters of the machine learning model to become iterativelyoptimized for transforming the first medical scanning domain to thesecond medical scanning domain. As those having ordinary skill in theart will appreciate, any suitable training batch sizes and/or trainingepochs can be implemented.

In embodiments where the segmentation component is implemented and wherethe denoise component is not implemented, the training component cantrain the machine learning model on the training dataset as follows. Invarious instances, the internal parameters (e.g., weights, biases) ofthe machine learning model can be randomly initialized. In variouscases, the training component can select a training image from thetraining dataset. In various aspects, the training component canregister/align the selected training image with its correspondingannotation image, if such registration/alignment has not already beenfacilitated. In various instances, the segmentation component canidentify, via execution of the segmentation model, a region-of-interestin the selected training image and can identify the sameregion-of-interest in the corresponding annotation image. Accordingly,the segmentation component can crop out of the selected training imageany portions of the selected training image that are not within theregion-of-interest. Similarly, the segmentation component can crop outof the corresponding annotation image any portions of the correspondingannotation image that are not within the region-of-interest. In variousaspects, the training component can feed the cropped selected trainingimage as input to the machine learning model. This can cause the machinelearning model to generate as output a cropped predicted image, wherethe cropped predicted image represents how the cropped selected trainingimage would look if the cropped selected training image werecaptured/generated via the second medical scanning domain rather thanthe first medical scanning domain. If the machine learning model has sofar undergone no and/or little training, the cropped predicted image canbe rather inaccurate. In various instances, the training component cancompute an error/loss between the cropped predicted image and thecropped corresponding annotation image. Accordingly, the trainingcomponent can update, via backpropagation, the internal parameters ofthe machine learning model based on the computed error/loss.

As mentioned above, the training component can repeat this procedure foreach training image in the training dataset, thereby causing theinternal parameters of the machine learning model to become iterativelyoptimized for transforming the first medical scanning domain to thesecond medical scanning domain. As also mentioned above, when themachine learning model is trained based on cropped versions of thetraining images and cropped versions of the annotation images, themachine learning model can be considered as being able to focus on theregion-of-interest, without getting distracted by background portions.Such focusing can cause the machine learning model to be trained morequickly and/or to achieve a higher degree of performance accuracy.Again, any suitable training batch sizes and/or training epochs can beimplemented.

In embodiments where the segmentation component and the denoisecomponent are both implemented, the training component can train themachine learning model on the training dataset as follows. In variousinstances, the internal parameters (e.g., weights, biases) of themachine learning model can be randomly initialized. In various cases,the training component can select a training image from the trainingdataset. In various aspects, the training component can register/alignthe selected training image with its corresponding annotation image, ifsuch registration/alignment has not already been facilitated. In variousinstances, the denoise component can evaluate the noise level of theselected training image and of the corresponding annotation image. Basedon such evaluation, the denoise component can apply one or moredenoising techniques to both the selected training image and thecorresponding annotation image, as appropriate. In various cases, thesegmentation component can identify, via execution of the segmentationmodel, a region-of-interest in the denoised selected training image andcan identify the same region-of-interest in the denoised correspondingannotation image. Accordingly, the segmentation component can crop outof the denoised selected training image any portions of the denoisedselected training image that are not within the region-of-interest.Similarly, the segmentation component can crop out of the denoisedcorresponding annotation image any portions of the denoisedcorresponding annotation image that are not within theregion-of-interest. In various aspects, the training component can feedthe denoised and cropped selected training image as input to the machinelearning model. This can cause the machine learning model to generate asoutput a denoised and cropped predicted image, where the denoised andcropped predicted image represents how the denoised and cropped selectedtraining image would look if the denoised and cropped selected trainingimage were captured/generated via the second medical scanning domainrather than the first medical scanning domain. If the machine learningmodel has so far undergone no and/or little training, the denoised andcropped predicted image can be rather inaccurate. In various instances,the training component can compute an error/loss between the denoisedand cropped predicted image and the denoised and cropped correspondingannotation image. Accordingly, the training component can update, viabackpropagation, the internal parameters of the machine learning modelbased on the computed error/loss.

As mentioned above, the training component can repeat this procedure foreach training image in the training dataset, thereby causing theinternal parameters of the machine learning model to become iterativelyoptimized for transforming the first medical scanning domain to thesecond medical scanning domain. As also mentioned above, when themachine learning model is trained based on denoised and cropped versionsof the training images and denoised and cropped versions of theannotation images, the machine learning model can be considered as beingable to focus on the region-of-interest, without getting distracted bybackground portions and/or by noise. Again, any suitable training batchsizes and/or training epochs can be implemented.

Those having ordinary skill in the art will appreciate that, when thedenoise component is implemented with the segmentation component, thedenoise component can apply the one or more denoising techniques to theselected training image and/or to the corresponding annotation imagebefore the segmentation component crops such images (as described above)and/or after the segmentation component crops such images. That is, theorder of application of denoising and segmentation can be switched inany suitable fashion as desired.

Moreover, those having ordinary skill in the art will appreciate thatthe training component can, in various embodiments, implement anysuitable loss, error, and/or objective functions to facilitate trainingof the machine learning model. In some cases, the loss, error, and/orobjective function can be customized and/or otherwise based onoperational context of the machine learning model. For instance, anysuitable constraints can be added to and/or otherwise incorporated intothe loss, error, and/or objective function, based on the specificcomputerized diagnostic technique that is desired to be applied to theoutput of the machine learning model. As a non-limiting example, supposethat it is desired to apply Agatston scoring to the predicted image thatis outputted by the machine learning model during inferencing. In suchcases, because Agatston scoring is a type of calcium scoring technique,it can be desired that the machine learning model be highly accuratewhen shifting the medical scanning domain of depicted calcificationregions. Thus, higher weights can be assigned to such calcificationregions in the loss, error, and/or objective function, and lower weightscan be assigned to non-calcification regions, so that the training ofthe machine learning model is biased toward getting higher accuracy onthe calcification regions. Moreover, since Agatston scoring works byclassifying different pixels/voxels into four different calcificationcategories, it can be desired to ensure that the machine learning modeltransforms intensities of pixels/voxels to be in accordance with thetarget medical scanning domain without changing the Agatston categoriesof such pixels/voxels. Accordingly, the loss, error, and/or objectivefunction can include a penalty that is applied whenever the machinelearning model causes an Agatston category change during training, so asto bias the machine learning model against causing such categorychanges. Those having ordinary skill in the art will appreciate thatthese are mere non-limiting examples of ways in which the loss, error,and/or objective function can be customized and/or manipulated, based onthe desired diagnostic technique to be applied to the inferencing outputof the machine learning model.

In various aspects, the computerized tool described herein canelectronically receive a medical image that has been captured/generatedvia an input medical scanning domain and can electronically execute amachine learning model on the medical image, wherein the machinelearning model is configured to translate and/or transform the inputmedical scanning domain into a target medical scanning domain. Thus,diagnostic techniques that can only be applied to medical images thatare captured/generated via the target medical scanning domain cannevertheless be implemented.

Various embodiments of the subject innovation can be employed to usehardware and/or software to solve problems that are highly technical innature (e.g., to facilitate learning-based domain transformation formedical images), that are not abstract and that cannot be performed as aset of mental acts by a human. Further, some of the processes performedcan be performed by a specialized computer (e.g., domain transformationmachine learning model, pre-trained segmentation model, pre-trainedregistration model) for carrying out defined tasks related tolearning-based domain transformation for medical images. For example,such defined tasks can include: accessing, by a device operativelycoupled to a processor, a medical image, wherein the medical imagedepicts an anatomical structure according to a first medical scanningdomain; and generating, by the device and via execution of a machinelearning model, a predicted image based on the medical image, whereinthe predicted image depicts the anatomical structure according to asecond medical scanning domain that is different from the first medicalscanning domain. Such defined tasks are not performed manually byhumans. Indeed, neither the human mind nor a human with pen and papercan electronically receive a medical image that has beencaptured/generated via a given medical scanning domain and canelectronically execute a machine learning model on the medical image togenerate a predicted image, where the predicted image can be consideredas representing how the medical image would have looked if it had beencaptured/generated via a different medical scanning domain. Instead,various embodiments of the subject innovation are inherently andinextricably tied to computer technology and cannot be implementedoutside of a computing environment (e.g., machine learning models areconcrete and tangible combinations of computer-executable hardwareand/or computer-executable software; thus, a computerized tool thatexecutes a domain transformation machine learning model on a medicalimage so as to shift and/or transform the medical scanning domain of themedical image is itself an inherently-computerized device that cannot bepracticably implemented in any sensible way without computers).

Moreover, various embodiments of the subject innovation can integrateinto a practical application various teachings described herein relatingto the field of medical imaging. As explained above, there exist variousdifferent medical scanning domains (e.g., high electrical energy vs. lowelectrical energy, arterial contrast phase vs. delayed contrast phase)that can be implemented in various different contexts. Specifically, itis often the case that a diagnostic technique is desired to be appliedto a medical image and that such diagnostic technique assumes that themedical image was captured/generated via a certain medical scanningdomain. However, there may be any number of reasons why the certainmedical scanning domain is unavailable (e.g., using high electricalenergy and/or high radiation can be dangerous to the health of thepatient). In such case, the medical image would have to becaptured/generated via some other medical scanning domain. When theother medical scanning domain is used, existing techniques offer nosolution for accurately and/or reliably applying the desired diagnostictechnique to the medical image. In stark contrast, various embodimentsof the subject innovation can solve this problem. Specifically, thecomputerized tool described herein can execute a machine learning modelon the medical image, and the output of such machine learning model canbe and/or represent how the medical image would have looked if it hadbeen captured/generated by the certain medical scanning domain ratherthan the other medical scanning domain. So, the desired diagnostictechnique can be accurately and/or reliably applied to the predictedimage. In other words, the computerized tool described herein cantransform an inputted medical image from one medical scanning domain toanother medical scanning domain, so that diagnostic techniques that canonly be used in conjunction with the another medical scanning domain canbe applied to the medical image. Accordingly, the computerized tooldescribed herein can be considered as a concrete and tangible technicalimprovement in the field of medical imaging, and thus clearlyconstitutes a useful and practical application of computers.

Furthermore, various embodiments of the subject innovation can controlreal-world tangible devices based on the disclosed teachings. Forexample, various embodiments of the subject innovation canelectronically execute a real-world domain transformation machinelearning model on a real-world medical image, so as to generate areal-world predicted image that represents how the real-world medicalimage would have looked in a different medical scanning domain.Moreover, the computerized tool can, in some cases, actually execute oneor more real-world diagnostic techniques (e.g., Agatston scoring) on thereal-world predicted image, so as to yield real-worlddiagnosis/prognosis results for a real-world medical patient.

It should be appreciated that the herein figures and description providenon-limiting examples of the subject innovation and are not necessarilydrawn to scale.

FIG. 1 illustrates a block diagram of an example, non-limiting system100 that can facilitate learning-based domain transformation for medicalimages in accordance with one or more embodiments described herein. Asshown, a learning-based domain transformation system 102 can beelectronically integrated, via any suitable wired and/or wirelesselectronic connection, with a medical image 104.

In various embodiments, the medical image 104 can depict an anatomicalstructure of a patient (e.g., human, animal, and/or otherwise). Invarious aspects, the anatomical structure can be any suitable body part,and/or portion thereof, of the patient. For example, the anatomicalstructure can be a head, a neck, a chest, an abdomen, an arm, a leg, ahand, a foot, a brain, an eye, an ear canal, a tongue, an esophagus, atrachea, a heart, a lung, a stomach, an intestine, a vein, an artery, abone, and/or any other suitable body part and/or portion thereof. Invarious instances, the medical image 104 can be any suitable type ofmedical image that is captured/generated by any suitable medicalscanning modality. As a non-limiting example, the medical image 104 canbe a CT image that is captured/generated by a CT scanner. As anotherexample, the medical image 104 can be an MRI image that iscaptured/generated by an MRI scanner. As yet another example, themedical image 104 can be an X-ray image that is captured/generated by anX-ray scanner. As still another example, the medical image 104 can be anultrasound image that is captured/generated by an ultrasound scanner. Aseven another example, the medical image 104 can be a PET image that iscaptured/generated by a PET scanner.

In various cases, the medical image 104 can have any suitabledimensionality. For example, in some cases, the medical image 104 can bea two-dimensional array of pixels, where each pixel exhibits aHounsfield unit (HU) intensity value. As another example, in othercases, the medical image 104 can be a three-dimensional array of voxels,where each voxel exhibits an HU intensity value. For ease ofexplanation, the herein disclosure mainly describes the medical image104 as a two-dimensional array of pixels. However, those having ordinaryskill in the art will appreciate that the herein teachings are equallyapplicable to three-dimensional arrays of voxels. Indeed, atwo-dimensional slice (e.g., axial slice, coronal slice, sagittal slice)of a three-dimensional array of voxels can itself be considered as atwo-dimensional array of pixels.

In various aspects, the medical image 104 can depict the anatomicalstructure according to a first medical scanning domain 106. In variousinstances, the first medical scanning domain 106 can be any suitableconfigurable setting, configurable control, and/or configurableparameter of the medical scanning modality that captured/generated themedical image 104, where such configurable setting, configurablecontrol, and/or configurable parameter can influence and/or otherwiseaffect how the medical image 104 visually appears oncecaptured/generated. As a non-limiting example, the first medicalscanning domain 106 can be an electrical energy level with which themedical scanning modality captures/generates the medical image 104.After all, capturing/generating the medical image 104 via a lowelectrical energy level (e.g., 70 kVp) can cause the medical image 104to depict the anatomical structure using a given HU intensitydistribution, whereas capturing/generating the medical image 104 via ahigh electrical energy level (e.g., 120 kVp) can cause the medical image104 to depict the anatomical structure using a different HU intensitydistribution. As another non-limiting example, the first medicalscanning domain 106 can be a contrast phase with which the medicalscanning modality captures/generates the medical image 104. After all,capturing/generating the medical image 104 via a first contrast phase(e.g., one of non-contrast, early arterial phase, late arterial phase,hepatic phase, nephrogenic phase, venous phase, and/or delayed phase)can cause the medical image 104 to depict the anatomical structure usinga given HU intensity distribution, whereas capturing/generating themedical image 104 via a different contrast phase (e.g., a different oneof non-contrast, early arterial phase, late arterial phase, hepaticphase, nephrogenic phase, venous phase, and/or delayed phase) can causethe medical image 104 to depict the anatomical structure using adifferent HU intensity distribution. Those having ordinary skill in theart will understand that, in some cases, the first medical scanningdomain 106 can be both an electrical energy level and a contrast phase(e.g., a single CT image can be captured/generated at both a givenelectrical energy level and a given contrast phase). In such cases, thefirst medical scanning domain 106 can be considered and/or referred toas an energy-contrast pair.

Accordingly, in various aspects, the first medical scanning domain 106can be a first electrical energy level and/or a first contrast phasethat is utilized by the medical scanning modality to capture/generatethe medical image 104.

In various cases, it can be desired to apply a computerized diagnostictechnique to the medical image 104. In various cases, the computerizeddiagnostic technique can be any suitable model and/or algorithm that isconfigured to identify one or more pathologies in the medical image 104.As a non-limiting example, the computerized diagnostic technique can bea tumor detection algorithm. As another example, the computerizeddiagnostic technique can be an occlusion detection algorithm. As stillanother example, the computerized diagnostic technique can be acalcification scoring algorithm, such as Agatston scoring. In any case,the computerized diagnostic technique can assume and/or otherwise relyon the assumption that the medical image 104 is captured/generated by asecond medical scanning domain that is different from the first medicalscanning domain 106. For example, if the first medical scanning domain106 is a first electrical energy level, then the second medical scanningdomain can be a different electrical energy level. As another example,if the first medical scanning domain 106 is a first contrast phase, thenthe second medical scanning domain can be a different contrast phase. Asstill another example, if the first medical scanning domain 106 is afirst energy-contrast pair, then the second medical scanning domain canbe a different energy-contrast pair. Because the first medical scanningdomain 106 does not match the medical scanning domain that is assumed bythe computerized diagnostic technique, the computerized diagnostictechnique can be unable to be reliably and/or accurately applied to themedical image 104. In various cases, the learning-based domaintransformation system 102 can solve this problem.

In various embodiments, the learning-based domain transformation system102 can comprise a processor 108 (e.g., computer processing unit,microprocessor) and a computer-readable memory 110 that is operablyand/or operatively and/or communicatively connected/coupled to theprocessor 108. The computer-readable memory 110 can storecomputer-executable instructions which, upon execution by the processor108, can cause the processor 108 and/or other components of thelearning-based domain transformation system 102 (e.g., receivercomponent 112, transformation component 114) to perform one or moreacts. In various embodiments, the computer-readable memory 110 can storecomputer-executable components (e.g., receiver component 112,transformation component 114), and the processor 108 can execute thecomputer-executable components.

In various embodiments, the learning-based domain transformation system102 can comprise a receiver component 112. In various aspects, thereceiver component 112 can electronically receive and/or otherwiseelectronically access the medical image 104. In various instances, thereceiver component 112 can electronically retrieve the medical image 104from any suitable data structure, database, and/or computing device (notshown) that is electronically accessible to the receiver component 112.For example, the receiver component 112 can electronically retrieve themedical image 104 from the medical scanning modality (e.g., CT scanner,MRI scanner, X-ray scanner, ultrasound scanner, PET scanner) thatcaptured/generated the medical image 104. In any case, the receivercomponent 112 can electronically obtain and/or access the medical image104, such that other components of the learning-based domaintransformation system 102 can electronically interact with the medicalimage 104.

In various embodiments, the learning-based domain transformation system102 can comprise a transformation component 114. In various aspects, thetransformation component 114 can electronically store, maintain,control, and/or otherwise access a machine learning model. In variousinstances, the machine learning model can be configured to transform,translate, shift, and/or otherwise convert the first medical scanningdomain 106 of the medical image 104 into the second medical scanningdomain. More specifically, the machine learning model can be configuredto receive as input the medical image 104 and to produce as output apredicted image, where the predicted image depicts the same anatomicalstructure as the medical image 104, but where the predicted imageexhibits an HU intensity distribution according to the second medicalscanning domain, rather than according to the first medical scanningdomain 106. In other words, the predicted image can be considered asrepresenting how the medical image 104 would look if it had beencaptured/generated according to the second medical scanning domaininstead of the first medical scanning domain 106. Accordingly, thecomputerized diagnostic technique, which can only be accurately appliedto medical images that are captured/generated via the second medicalscanning domain, can be accurately applied to the predicted image. Inthis way, the medically-relevant information that is represented by themedical image 104 can be analyzed by the computerized diagnostictechnique, notwithstanding that the medical image 104 iscaptured/generated via a medical scanning domain that is not consistentwith the underlying assumptions of the computerized diagnostictechnique. This certainly constitutes a concrete and tangible technicalimprovement.

Although not shown in FIG. 1 , the learning-based domain transformationsystem 102 can, in various embodiments, comprise an execution component.In various aspects, the execution component can electronically apply thecomputerized diagnostic technique to the predicted image that isoutputted by the machine learning model of the transformation component114, thereby yielding one or more diagnostic/prognostic results. Invarious instances, the execution component can electronically transmitsuch diagnostic/prognostic results to any suitable computing device.

Although not explicitly shown in FIG. 1 , the learning-based domaintransformation system 102 can comprise a segmentation component. Invarious aspects, the segmentation component can electronically store,maintain, control, and/or otherwise access a pre-trained segmentationmodel. In various instances, the pre-trained segmentation model can beconfigured to receive as input the medical image 104 and to identify asoutput a region-of-interest that is depicted within the medical image104. In some cases, the region-of-interest can be the anatomicalstructure. In other cases, the region-of-interest can be a specificsub-portion of the anatomical structure. In any case, the pre-trainedsegmentation model can be configured to identify and/or mask theregion-of-interest, and the segmentation component can electronicallycrop out of the medical image 104 any pixels/voxels that do not make upthe region-of-interest. In this way, background portions of the medicalimage 104 can be eliminated and/or deleted, so that only theregion-of-interest remains in the medical image 104. In suchembodiments, the machine learning model can be configured to receive asinput the cropped version of the medical image 104, and the predictedimage that is outputted by the machine learning model can have the samedimensions (e.g., same number and/or arrangement of pixels/voxels) asthe cropped version of the medical image 104. In various cases, byconfiguring the machine learning model to operate on the cropped versionof the medical image 104, the machine learning model can be consideredas focusing on shifting the medical scanning domain of theregion-of-interest, without getting distracted by unimportant backgroundportions of the medical image 104. In other words, the machine learningmodel can, in some cases, achieve higher performance accuracy whenconfigured to receive as input the cropped version of the medical image104.

Although not explicitly shown in FIG. 1 , the learning-based domaintransformation system 102 can further comprise a denoise component. Invarious aspects, the denoise component can electronically evaluateand/or measure a visual noise level that is exhibited by the medicalimage 104. If the visual noise level does not exceed any suitablethreshold value, the denoise component can refrain from taking furtheraction. In such case, the segmentation component can crop the medicalimage 104 so that it depicts only the region-of-interest, and thetransformation component 114 can execute the machine learning model onthe cropped version of the medical image 104. On the other hand, if thevisual noise level exceeds any suitable threshold value, the denoisecomponent can electronically apply any suitable denoising technique(e.g., denoising filter, deep learning denoising model) to the medicalimage 104, so as to reduce and/or eliminate the visual noise of themedical image 104. After such denoising, the segmentation component cancrop the denoised version of the medical image 104 so that it depictsonly the region-of-interest, and the transformation component 114 canexecute the machine learning model on the cropped and denoised versionof the medical image 104. As those having ordinary skill in the art willappreciate, application of denoising to the medical image 104 can allowthe machine learning model to achieve a higher degree of performanceaccuracy. As those having ordinary skill in the art will furtherappreciate, in some cases, the denoise component can apply the denoisingtechnique to the medical image 104 prior to cropping by the segmentationcomponent, and in other cases, the segmentation component can crop themedical image 104 prior to application of the denoising technique by thedenoise component.

Although not explicitly shown in FIG. 1 , the learning-based domaintransformation system 102 can comprise a training component. In variousaspects, the receiver component 112 can electronically receive and/oraccess a training dataset, and the training component can electronicallytrain the machine learning model on the training dataset to accuratelyshift, covert, and/or transform the first medical scanning domain 106 tothe second medical scanning domain. More specifically, the trainingdataset can include a set of training images, each of which iscaptured/generated via the first medical scanning domain 106, and arespectively corresponding set of annotation images, each of which iscaptured/generated via the second medical scanning domain. Consider agiven training image in the set of training images, where the giventraining image depicts one or more anatomical structures according tothe first medical scanning domain 106. In such case, there can be agiven annotation image in the set of annotation images that correspondsto the given training image, where the given annotation image depictsthe same one or more anatomical structures as the given training image,but where the given annotation image depicts such one or more anatomicalstructures according to the second medical scanning domain instead ofthe first medical scanning domain 106. In other words, the givenannotation image can be considered as representing how the giventraining image would actually look if the given training image had beencaptured/generated via the second medical scanning domain. In variousaspects, the training component can input the given training image (withor without cropping by the segmentation component and/or denoising bythe denoise component, as desired) to the machine learning model. Thiscan cause the machine learning model to generate a predicted outputimage, where the predicted output image represents the machine learningmodel’s estimation of how the given training image would look ifcaptured/generated according to the second medical scanning domain. Invarious instances, the training component can compute an error/lossbetween the predicted output image and the given annotation image, andthe training component can update (e.g., via backpropagation) internalparameters of the machine learning model based on such error/loss. Invarious cases, the training component can repeat this for each trainingimage in the set of training images, with the ultimate result being thatthe internal parameters of the machine learning model become optimized.In other words, such training can cause the machine learning model tolearn how to accurately transform and/or convert the first medicalscanning domain 106 to the second medical scanning domain. Those havingordinary skill in the art will appreciate that any suitable batch sizes,any suitable number of epochs, and/or any suitable style of error/lossfunction can be implemented as desired.

FIG. 2 illustrates a block diagram of an example, non-limiting system200 including a machine learning model that can facilitatelearning-based domain transformation for medical images in accordancewith one or more embodiments described herein. As shown, the system 200can, in some cases, comprise the same components as the system 100, andcan further comprise a machine learning model 202 and/or a predictedimage 204.

In various embodiments, the transformation component 114 canelectronically store, electronically maintain, electronically operate,and/or otherwise electronically access the machine learning model 202.In various aspects, the machine learning model 202 can exhibit anysuitable artificial intelligence architecture. As a non-limitingexample, the machine learning model 202 can be a deep learningregression model that comprises any suitable number of neural networklayers, that comprises any suitable numbers of neurons in various layers(e.g., different layers can have different numbers of neurons), thatcomprises any suitable activation functions (e.g., softmax, sigmoid,hyperbolic tangent, rectified linear unit) in various neurons (e.g.,different neurons can have different activation functions), and/or thatcomprises any suitable interneuron connectivity pattern (e.g., forwardconnections, skip connections, recursive connections). In any case, thetransformation component 114 can electronically execute the machinelearning model 202 on the medical image 104, thereby yielding thepredicted image 204. In other words, the machine learning model 202 canbe configured to receive as input the medical image 104 and to produceas output the predicted image 204. For example, if the machine learningmodel 202 is a deep learning regression model, the medical image 104 cancomplete a forward pass through an input layer, one or more hiddenlayers, and an output layer of the machine learning model 202, with theresult generated by the output layer being the predicted image 204.

As mentioned above, the medical image 104 can depict an anatomicalstructure of a patient according to the first medical scanning domain106. In various aspects, the predicted image 204 can depict the sameanatomical structure of the patient, but the predicted image 204 can doso according to a second medical scanning domain 206, where the secondmedical scanning domain 206 is different from the first medical scanningdomain 106. As an example, if the first medical scanning domain 106 is aparticular electrical energy level (e.g., 70 kVp), the second medicalscanning domain 206 can be a different electrical energy level (e.g.,120 kVp). As another example, if the first medical scanning domain 106is a particular contrast phase (e.g., one of non-contrast, earlyarterial phase, late arterial phase, hepatic phase, nephrogenic phase,venous phase, and/or delayed phase), the second medical scanning domain206 can be a different contrast phase (e.g., a different one ofnon-contrast, early arterial phase, late arterial phase, hepatic phase,nephrogenic phase, venous phase, and/or delayed phase). As yet anotherexample, if the first medical scanning domain 106 is a particularenergy-contrast pair (e.g., a CT image captured/generated at 70 kVpduring the late arterial contrast phase), the second medical scanningdomain 206 can be a different energy-contrast pair (e.g., appearing tobe a CT image captured/generated at 120 kVp during a non-contrastphase).

In other words, the medical image 104 can exhibit an HU intensitydistribution that visually resembles the anatomical structure of thepatient and that is consistent with and/or otherwise determined by thefirst medical scanning domain 106, while the predicted image 204 canexhibit an HU intensity distribution that visually resembles the sameanatomical structure of the same patient but that is instead consistentwith and/or otherwise determined by the second medical scanning domain206. In still other words, the predicted image 204 can be considered asshowing and/or otherwise representing how the medical image 104 wouldhave visually appeared and/or looked if the medical image 104 had beencaptured/generated via the second medical scanning domain 206 ratherthan the first medical scanning domain 106.

Those having ordinary skill in the art will appreciate that thepredicted image 204 can, in various aspects, have the same format as themedical image 104. For example, if the medical image 104 is a CT imagecaptured/generated via the first medical scanning domain 106, then thepredicted image 204 can likewise appear to be a CT image, but one thatis captured/generated via the second medical scanning domain 206 insteadof the first medical scanning domain 106. As another example, if themedical image 104 is an X-ray image captured/generated via the firstmedical scanning domain 106, then the predicted image 204 can likewiseappear to be an X-ray image, but one that is captured/generated via thesecond medical scanning domain 206 rather than the first medicalscanning domain 106. As yet another example, if the medical image 104 isan MRI image captured/generated via the first medical scanning domain106, then the predicted image 204 can likewise appear to be an MRIimage, but one that is captured/generated via the second medicalscanning domain 206 instead of the first medical scanning domain 106. Asstill another example, if the medical image 104 is an ultrasound imagecaptured/generated via the first medical scanning domain 106, then thepredicted image 204 can likewise appear to be an ultrasound image, butone that is captured/generated via the second medical scanning domain206 rather than the first medical scanning domain 106. As even anotherexample, if the medical image 104 is a PET image captured/generated viathe first medical scanning domain 106, then the predicted image 204 canlikewise appear to be a PET image, but one that is captured/generatedvia the second medical scanning domain 206 instead of the first medicalscanning domain 106.

Those having ordinary skill in the art will further appreciate that thepredicted image 204 can, in various aspects, have the samedimensionality as the medical image 104. For example, if the medicalimage 104 is a two-dimensional array of pixels, then the predicted image204 can likewise be a two-dimensional array of pixels (e.g., thepredicted image 204 can have the same number and/or arrangement ofpixels as the medical image 104). As another example, if the medicalimage 104 is a three-dimensional array of voxels, then the predictedimage 204 can likewise be a three-dimensional array of voxels (e.g., thepredicted image 204 can have the same number and/or arrangement ofvoxels as the medical image 104).

FIG. 3 illustrates an example, non-limiting block diagram 300 showinghow the machine learning model 202 can transform the domain of themedical image 104 in accordance with one or more embodiments describedherein.

In the non-limiting example shown in FIG. 3 , the medical image 104 canbe an axial CT image depicting a patient’s chest cavity and heart, thatwas captured/generated at an electrical energy level of 70 kVp. That is,the first medical scanning domain 106 in this non-limiting example canbe an electrical energy level of 70 kVp. Those having ordinary skill inthe art will recognize that the HU intensity distribution of the medicalimage 104, as shown in FIG. 3 , looks and/or appears to be consistentwith an electrical energy level of 70 kVp.

In various aspects, it can be desired to approximate and/or estimatewhat the medical image 104 would have looked like if it had instead beencaptured/generated at 120 kVp. Accordingly, the machine learning model202 can be configured and/or trained (as described in more detail belowwith respect to FIGS. 8-9 ) to transform and/or convert CT scans takenat 70 kVp to CT scans taken at 120 kVp. Thus, in various cases, thetransformation component 114 can feed the medical image 104 to themachine learning model 202 as input, and the machine learning model 202can produce as output the predicted image 204. As shown in FIG. 3 , thepredicted image 204 can visually depict the same axial view of the chestcavity and heart of the patient as the medical image 104, but thepredicted image 204 can look and/or appear to have beencaptured/generated at an electrical energy level of 120 kVp rather thanat an electrical energy level of 70 kVp. That is, the second medicalscanning domain 206 in this non-limiting example can be an electricalenergy level of 120 kVp. In other words, the predicted image 204 that isshown in FIG. 3 can be considered as representing how the medical image104 depicting the patient’s chest cavity and heart would have looked ifthe medical image 104 had been originally captured/generated at 120 kVp.Those having ordinary skill in the art will recognize that the HUintensity distribution of the predicted image 204, as shown in FIG. 3 ,looks and/or appears to be consistent with an electrical energy level of120 kVp.

In current clinical practice, a CT image that is captured/generated at70 kVp can be considered as a low-power and/or low-dose CT scan, whereasa CT image that is captured/generated at 120 kVp can be considered as ahigh-power and/or high-dose CT scan. Exposing a patient to a low-powerand/or low-dose CT scan can be safer from the perspective of thepatient’s health (e.g., less exposure to cancer-causing radiation) butcan be suboptimal from the perspective of diagnosis/prognosis (e.g., alow-power and/or low-dose CT scan can exhibit decreased visual quality;preferred/desired diagnostic techniques cannot be reliably applied to alow-power and/or low-dose CT scan). On the other hand, exposing apatient to a high-power and/or high-dose CT scan can be desirable fromthe perspective of diagnosis/prognosis (e.g., a high-power and/orhigh-dose CT scan can exhibit increased visual quality;preferred/desired diagnostic techniques can be reliably applied to ahigh-power and/or high-dose CT scan) but can be suboptimal from theperspective of the patient’s health (e.g., increased exposure tocancer-causing radiation). Existing techniques in clinical practiceoffer no solution to resolve this conflict.

Various embodiments of the subject innovation, on the other hand, canaddress this problem. Specifically, a low-power/low-dose CT scan (e.g.,medical image 104) can be captured/generated from the patient, and suchlow-power/low-dose CT scan can be fed as input to the machine learningmodel 202. This can cause the machine learning model 202 to produce asoutput an image (e.g., predicted image 204) that estimates and/orrepresents how the low-power/low-dose CT scan would have looked if ithad instead been originally captured/generated as a high-power/high-doseCT scan. In this way, the patient is not exposed to excessively highamounts of radiation, and yet an image that estimates and/orapproximates a high-power/high-dose CT scan of the patient isnevertheless ultimately obtained for purposes of diagnosis and/orprognosis. This certainly constitutes a concrete and tangible technicalimprovement in the field of medical imaging.

Those having ordinary skill in the art will appreciate that FIG. 3illustrates a mere non-limiting example where the machine learning model202 is configured and/or trained to transform and/or convert CT imagesfrom an electrical energy level of 70 kVp to an electrical energy levelof 120 kVp. In various other embodiments, the machine learning model 202can be configured and/or trained to transform and/or convert CT imagesfrom any electrical energy level to any other electrical energy level.In still other embodiments, the machine learning model 202 can beconfigured and/or trained to transform and/or convert CT images from anycontrast phase to any other contrast phase. In yet other embodiments,the machine learning model 202 can be configured and/or trained totransform and/or convert CT images from any energy-contrast pair to anyother energy-contrast pair. That is, those having ordinary skill in theart will understand that the machine learning model 202 can beconfigured and/or trained to transform and/or convert a CT image fromany suitable CT scanning domain to any other suitable CT scanningdomain, as desired. Moreover, in various aspects, those having ordinaryskill in the art will appreciate that the machine learning model 202 canbe configured to operate on any other suitable type of medical image(e.g., MRI, X-ray, PET, ultrasound) and not just CT images.

FIG. 4 illustrates a block diagram of an example, non-limiting system400 including a segmentation model that can facilitate learning-baseddomain transformation for medical images in accordance with one or moreembodiments described herein. As shown, the system 400 can, in somecases, comprise the same components as the system 200, and can furthercomprise a segmentation component 402.

In various embodiments, the segmentation component 402 canelectronically store, electronically maintain, electronically control,and/or otherwise electronically access a segmentation model 404. Invarious instances, the segmentation component 402 can electronicallyexecute the segmentation model 404 on the medical image 104, which cancause the segmentation model 404 to identify a region-of-interest 406that is depicted within the medical image 104. In various cases, theregion-of-interest 406 can be any suitable portion (e.g., any suitablesubset of pixels/voxels) of the medical image 104, that is deemed and/orconsidered to be medically important for purposes ofdiagnosis/prognosis. As an example, the region-of-interest 406 caninclude the anatomical structure that is depicted in the medical image104 and can exclude background portions of the medical image 104. Asanother example, the region-of-interest 406 can include a portion of theanatomical structure that is depicted in the medical image 104 and canexclude both a remainder of the anatomical structure and backgroundportions of the medical image 104. In various aspects, the segmentationmodel 404 can exhibit any suitable artificial intelligence architecture.For example, the segmentation model 404 can be a deep learningsegmentation model (e.g., including any suitable number of layers, anysuitable numbers of neurons in various layers, any suitable activationfunctions, and/or any suitable connectivity patterns). Moreover, invarious instances, the segmentation model 404 can be trained to identifythe region-of-interest 406 via any suitable training paradigm (e.g.,supervised learning, unsupervised learning, reinforcement learning).

In various aspects, once the segmentation model 404 has identified theregion-of-interest 406 within the medical image 104, the segmentationcomponent 402 can electronically crop the medical image 104, such thatthe medical image 104 now contains only the region-of-interest 406 andno longer contains the background portions and/orregions-of-disinterest. In other words, the segmentation component 402can eliminate and/or delete any pixels/voxels of the medical image 104that do not make up the region-of-interest 406, and the segmentationcomponent 402 can refrain from eliminating/deleting any pixels/voxels ofthe medical image 104 that do make up the region-of-interest 406. In anycase, the segmentation component 402 can cause the medical image 104 todepict only and/or substantially only the region-of-interest 406.Accordingly, the transformation component 114 can execute the machinelearning model 202 on the region-of-interest 406 (e.g., on the croppedversion of the medical image 104), thereby yielding the predicted image204. Those having ordinary skill in the art will appreciate that, insuch cases, the predicted image 204 can have the same dimensionality asthe region-of-interest 406 (e.g., can the same number and/or arrangementof pixels/voxels as the cropped version of the medical image 104).

FIG. 5 illustrates an example, non-limiting block diagram 500 showinghow the segmentation model 404 and the machine learning model 202 cantransform the domain of the medical image 104 in accordance with one ormore embodiments described herein.

As shown in the non-limiting example of FIG. 5 , the medical image 104can be an axial CT image depicting a patient’s chest cavity and heart,that was captured/generated at an electrical energy level of 70 kVp.That is, the medical image 104 of FIG. 5 can be the same as the medicalimage 104 of FIG. 3 . In various aspects, the segmentation component 402can execute the segmentation model 404 on the medical image 104, therebycausing the segmentation model 404 to identify the region-of-interest406 within the medical image 104. Moreover, the segmentation component402 can crop out background portions of the medical image 104, such thatonly the region-of-interest 406 remains in the cropped version of themedical image 104. As shown in FIG. 5 , the region-of-interest 406 inthis non-limiting example can include the heart that is depicted in themedical image 104 and can exclude the surrounding chest cavity (e.g.,the ribs and vertebrae) that is depicted around in the heart in themedical image 104.

Note that, at this point, the region-of-interest 406 (e.g., the croppedversion of the medical image 104) can still exhibit an HU intensitydistribution that corresponds to an electrical energy level of 70 kVp.Accordingly, in various embodiments, the transformation component 114can execute the machine learning model 202 on the region-of-interest 406(e.g., on the cropped version of the medical image 104), thereby causingthe machine learning model 202 to output the predicted image 204. Asshown, in various cases, the predicted image 204 can depict the sameview of the heart as the region-of-interest 406 (e.g., as the croppedversion of the medical image 104), but the predicted image 204 can lookand/or appear to have been captured/generated at an electrical energylevel of 120 kVp instead of 70 kVp. Those having ordinary skill in theart will appreciate that, in such cases, the predicted image 204 canhave the same dimensionality as the region-of-interest 406 (e.g., as thecropped version of the medical image 104), rather than the full medicalimage 104. Furthermore, those having ordinary skill in the art willappreciate that, by configuring the machine learning model 202 tooperate/execute on the region-of-interest 406 (e.g., on the croppedversion of the medical image 104) rather than on the entirety of themedical image 104, the machine learning model 202 can avoid gettingslowed down and/or distracted by background portions of the medicalimage 104. That is, when cropping by the segmentation component 402 isimplemented, the machine learning model 202 can achieve betterperformance.

FIG. 6 illustrates a block diagram of an example, non-limiting system600 including a denoise component that can facilitate learning-baseddomain transformation for medical images in accordance with one or moreembodiments described herein. As shown, the system 600 can, in somecases, comprise the same components as the system 400, and can furthercomprise a denoise component 602.

In various embodiments, the denoise component 602 can electronicallyapply one or more denoising techniques to the medical image 104, priorto the execution of the machine learning model 202. More specifically,in various aspects, the denoise component 602 can electronically measureand/or otherwise electronically determine a level of noise exhibited bythe medical image 104. If the level of noise is lesser than any suitablethreshold, the denoise component 602 can refrain from taking furtheraction. In such case, the segmentation component 402 can identify theregion-of-interest 406 in the medical image 104 and can crop the medicalimage 104 accordingly, and the transformation component 114 can executethe machine learning model 202 on the region-of-interest 406 (e.g., onthe cropped version of the medical image 104). On the other hand, if thelevel of noise is greater than any suitable threshold, the denoisecomponent 602 can apply the one or more denoising techniques to themedical image 104, thereby reducing the level of noise of the medicalimage 104. In such case, the segmentation component 402 can identify theregion-of-interest 406 in the denoised version of the medical image 104and can crop the denoised version of the medical image 104 accordingly,and the transformation component 114 can execute the machine learningmodel 202 on the region-of-interest 406 (e.g., on the denoised andcropped version of the medical image 104).

Those having ordinary skill in the art will appreciate that the denoisecomponent 602 can implement any suitable denoising techniques as desired(e.g., denoising filters, deep learning denoising models). Those havingordinary skill in the art will further appreciate that the denoisecomponent 602 can, in some embodiments, apply the one or more denoisingtechniques after the segmentation component 402 crops the medical image104. That is, the denoise component 602 can evaluate the noise level ofthe cropped version of the medical image 104 and apply the one or moredenoising techniques to the cropped version of the medical image 104accordingly. In any case, the denoise component 602 can ensure, prior toexecution of the machine learning model 202, that the medical image 104(e.g., that the cropped version of the medical image 104) does notexhibit excessive noise. This can allow the machine learning model 202to achieve higher performance accuracy upon execution.

FIG. 7 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 700 that can facilitate learning-baseddomain transformation for medical images in accordance with one or moreembodiments described herein. In various cases, the learning-baseddomain transformation system 102 can facilitate the computer-implementedmethod 700.

In various embodiments, act 702 can include receiving, by a device(e.g., 112) operatively coupled to a processor, a medical image (e.g.,104) having an intensity distribution corresponding to a first medicalscanning domain (e.g., 106). As an example, the first medical scanningdomain can be a low electrical energy level and/or a particular contrastphase (e.g., arterial contrast phase).

In various aspects, act 704 can include determining, by the device(e.g., 602), whether a noise level of the medical image exceeds athreshold. If so, the computer-implemented method 700 can proceed to act706. If not, the computer-implemented method 700 can proceed to act 708.

In various instances, act 706 can include applying, by the device (e.g.,602), a denoising technique to the medical image. This can ensure thatthe medical image does not exhibit excessive noise that might otherwiseconfound down-stream analysis.

In various cases, act 708 can include executing, by the device (e.g.,402), a segmentation model (e.g., 404) on the medical image, therebyidentifying a region-of-interest (e.g., 406) within the medical image.

In various aspects, act 710 can include cropping out, by the device(e.g., 402), portions of the medical image that are outside of theregion-of-interest. This can ensure that the medical image no longerincludes unimportant parts that might otherwise confound downstreamanalysis.

In various instances, act 712 can include executing, by the device(e.g., 114), a domain-transformation model (e.g., 202) on the medicalimage, thereby yielding a predicted image (e.g., 204) that has anintensity distribution corresponding to a second medical scanning domain(e.g., 206). As an example, if the first medical scanning domain is alow electrical energy level (e.g., 70 kVp), then the second medicalscanning domain can be a high electrical energy level (e.g., 120 kVp).As another example, if the first medical scanning domain is a particularcontrast phase (e.g., arterial phase), then the second medical scanningdomain can be a different contrast phase (e.g., hepatic phase).

In various aspects, FIGS. 1-7 and associated text can be considered asdescribing how the machine learning model 202 functions duringinferencing. However, before the machine learning model 202 can bedeployed and/or inferenced, the machine learning model 202 should betrained. Such training is described with respect to FIGS. 8-9 .

FIG. 8 illustrates a block diagram of an example, non-limiting system800 including a training component that can facilitate learning-baseddomain transformation for medical images in accordance with one or moreembodiments described herein. As shown, the system 800 can, in somecases, comprise the same components as the system 600, and can furthercomprise a training component 802 and/or a training dataset 804.

In various embodiments, the receiver component 112 can electronicallyreceive, retrieve, and/or access the training dataset 804 from anysuitable database, data structure, and/or computing device (not shown).In various aspects, the training component 802 can leverage the trainingdataset 804 to train the machine learning model 202 to accuratelytransform/convert the first medical scanning domain 106 to the secondmedical scanning domain 206, as described more thoroughly below.

In various instances, the training dataset 804 can include a set oftraining images 806 and a set of annotation images 808. In variousaspects, the set of training images 806 can include any suitable numberof training images. For instances, the set of training images 806 caninclude n training images, for any suitable positive integer n (e.g.,training image 1 to training image n). In various cases, the set ofannotation images 808 can respectively correspond to the set of trainingimages 806. That is, if the set of training images 806 has n trainingimages, then the set of annotation images 808 can have n annotationimages (e.g., annotation image 1 to annotation image n). In other words,there can be one unique annotation image per training image. Forexample, in various instances, the set of training images 806 caninclude a training image 1, and the set of annotation images 808 canlikewise include an annotation image 1 that corresponds to the trainingimage 1. Moreover, the set of training images 806 can include a trainingimage n, and the set of annotation images 808 can likewise includeannotation image n that corresponds to the training image n. In variouscases, each of the training images 806 can have the same format and/ordimensionality as the medical image 104. Moreover, each of the trainingimages 806 can have been captured/generated according to the firstmedical scanning domain 106. In stark contrast, each of the set ofannotation images 808 can have been captured/generated according to thesecond medical scanning domain 206.

More specifically, consider the training image 1 and the annotationimage 1. In various aspects, the training image 1 can depict one or moreparticular anatomical structures according to the first medical scanningdomain 106. In various instances, the annotation image 1 can depict thesame one or more particular anatomical structures as the training image1, but the annotation image 1 can do so according to the second medicalscanning domain 206. In other words, the training image 1 can be amedical scan that is captured/generated via the first medical scanningdomain 106, and the annotation image 1 can be a known ground-truth imagethat represents how the training image 1 should look and/or shouldvisually appear if it had been captured/generated via the second medicalscanning domain 206. Similarly, consider the training image n and theannotation image n. In various aspects, the training image n can depictone or more certain anatomical structures according to the first medicalscanning domain 106. In various instances, the annotation image n candepict the same one or more certain anatomical structures as thetraining image n, but the annotation image n can do so according to thesecond medical scanning domain 206. That is, the training image n can bea medical scan that is captured/generated via the first medical scanningdomain 106, and the annotation image n can be a known ground-truth imagethat represents how the training image n should look and/or shouldvisually appear if it had been captured/generated via the second medicalscanning domain 206.

In various aspects, each of the set of training images 806 can berespectively registered/aligned with corresponding ones of the set ofannotation images 808. That is, the training image 1 can be registeredand/or aligned in a pixel-to-pixel (and/or voxel-to-voxel) fashion withthe annotation image 1, and the training image n can be registeredand/or aligned in a pixel-to-pixel (and/or voxel-to-voxel) fashion withthe annotation image n. In some cases, such registration/alignment canbe facilitated at time of capture/generation of the training dataset 804(e.g., via dual-energy scanning modalities). In other cases, suchregistration/alignment can be facilitated by the training component 802.For example, the training component 802 can iteratively perturb (e.g.,rotate and/or translate) the training image 1 and/or the annotationimage 1 until pixel-wise (voxel-wise) alignment is achieved between thetraining image 1 and the annotation image 1. Similarly, the trainingcomponent 802 can iteratively perturb (e.g., rotate and/or translate)the training image n and/or the annotation image n until pixel-wise(voxel-wise) alignment is achieved between the training image n and theannotation image n. As yet another example, the training component 802can implement a pre-trained registration/alignment model (e.g., a deeplearning model) to facilitate such registration/alignment.

In embodiments where the segmentation component 402 and the denoisecomponent 602 are not implemented, the training component 802 can trainthe machine learning model 202 as follows. The internal parameters(e.g., weights, biases) of the machine learning model 202 can beinitialized in any suitable fashion (e.g., random initialization). Invarious instances, the training component 802 can select a trainingimage from the set of training images 806, and the training component802 can also select an annotation image from the set of annotationimages 808 that corresponds to the selected training image (e.g., theannotation image 1 corresponds to the training image 1). In variouscases, the transformation component 114 can feed the selected trainingimage to the machine learning model 202. The selected training image cancomplete a forward pass through the machine learning model 202, whichcan cause the machine learning model 202 to output a predicted imagebased on the selected training image (e.g., the predicted image can havethe same number and/or arrangement of pixels/voxels as the selectedtraining image). In various aspects, the predicted image can beconsidered as representing how the machine learning model 202 believesthat the selected training image would look if it had beencaptured/generated via the second medical scanning domain 206. On theother hand, the selected annotation image can represent how the selectedtraining image is actually known to look when captured/generated via thesecond medical scanning domain 206. Accordingly, the training component802 can compute an error/loss between the predicted image and theselected annotation image. In various cases, any suitable error/lossfunction can be implemented (e.g., any suitable weights and/orconstraints can be utilized as desired). Thus, the training component802 can update, via backpropagation, the internal parameters of themachine learning model 202 based on the computed error/loss. In variouscases, the training component 802 can repeat this procedure for eachtraining image in the set of training images 806, with the ultimateresult being that the internal parameters of the machine learning model202 become iteratively optimized. As those having ordinary skill in theart will appreciate, the training component 802 can implement any othersuitable training batch sizes and/or any suitable number of trainingepochs.

In embodiments where the segmentation component 402 is implemented butwhere the denoise component 602 is not implemented, the trainingcomponent 802 can train the machine learning model 202 as follows. Theinternal parameters of the machine learning model 202 can be initializedin any suitable fashion. In various instances, the training component802 can select a training image from the set of training images 806, andthe training component 802 can also select an annotation image from theset of annotation images 808 that corresponds to the selected trainingimage. In various cases, the segmentation component 402 can execute thesegmentation model 404 on the selected training image and/or on theselected annotation image. This can cause the segmentation model 404 toidentify a region-of-interest in the selected training image and toidentify the same region-of-interest in the selected annotation image.Thus, the segmentation component 402 can crop out of the selectedtraining image any pixels/voxels that are not within theregion-of-interest. Likewise, the segmentation component 402 can cropout of the selected annotation image any pixels/voxels that are notwithin the region-of-interest. In various aspects, the transformationcomponent 114 can feed the cropped version of the selected trainingimage to the machine learning model 202. The cropped version of theselected training image can complete a forward pass through the machinelearning model 202, which can cause the machine learning model 202 tooutput a predicted image based on the cropped version of the selectedtraining image (e.g., the predicted image can have the same numberand/or arrangement of pixels/voxels as the cropped version of theselected training image). In various aspects, the predicted image can beconsidered as representing how the machine learning model 202 believesthat the cropped version of the selected training image would look if ithad been captured/generated via the second medical scanning domain 206.On the other hand, the cropped version of the selected annotation imagecan represent how the cropped version of the selected training image isactually known to look when captured/generated via the second medicalscanning domain 206. Accordingly, the training component 802 can computean error/loss between the predicted image and the cropped version of theselected annotation image. In various cases, any suitable error/lossfunction can be implemented. Thus, the training component 802 canupdate, via backpropagation, the internal parameters of the machinelearning model 202 based on the computed error/loss. In various cases,the training component 802 can repeat this procedure for each trainingimage in the set of training images 806, with the ultimate result beingthat the internal parameters of the machine learning model 202 becomeiteratively optimized. As those having ordinary skill in the art willappreciate, the training component 802 can implement any other suitabletraining batch sizes and/or any suitable number of training epochs.

In embodiments where the segmentation component 402 and the denoisecomponent 602 are implemented, the training component 802 can train themachine learning model 202 as follows. The internal parameters of themachine learning model 202 can be initialized in any suitable fashion.In various instances, the training component 802 can select a trainingimage from the set of training images 806, and the training component802 can also select an annotation image from the set of annotationimages 808 that corresponds to the selected training image. In variouscases, the denoise component 602 can apply a denoising technique to theselected training image if a noise level of the selected training imageexceeds any suitable margin. Similarly, the denoise component 602 canapply a denoising technique to the selected annotation image if a noiselevel of the selected annotation image exceeds any suitable margin. Invarious instances, the segmentation component 402 can execute thesegmentation model 404 on the denoised version of the selected trainingimage and/or on the denoised version of the selected annotation image.This can cause the segmentation model 404 to identify aregion-of-interest in the denoised version of the selected trainingimage and to identify the same region-of-interest in the denoisedversion of the selected annotation image. Thus, the segmentationcomponent 402 can crop out of the denoised version of the selectedtraining image any pixels/voxels that are not within theregion-of-interest. Likewise, the segmentation component 402 can cropout of the denoised version of the selected annotation image anypixels/voxels that are not within the region-of-interest. In variousaspects, the transformation component 114 can feed the cropped anddenoised version of the selected training image to the machine learningmodel 202. The cropped and denoised version of the selected trainingimage can complete a forward pass through the machine learning model202, which can cause the machine learning model 202 to output apredicted image based on the cropped and denoised version of theselected training image (e.g., the predicted image can have the samenumber and/or arrangement of pixels/voxels as the cropped and denoisedversion of the selected training image). In various aspects, thepredicted image can be considered as representing how the machinelearning model 202 believes that the cropped and denoised version of theselected training image would look if it had been captured/generated viathe second medical scanning domain 206. On the other hand, the croppedand denoised version of the selected annotation image can represent howthe cropped and denoised version of the selected training image isactually known to look when captured/generated via the second medicalscanning domain 206. Accordingly, the training component 802 can computean error/loss between the predicted image and the cropped and denoisedversion of the selected annotation image. In various cases, any suitableerror/loss function can be implemented. Thus, the training component 802can update, via backpropagation, the internal parameters of the machinelearning model 202 based on the computed error/loss. In various cases,the training component 802 can repeat this procedure for each trainingimage in the set of training images 806, with the ultimate result beingthat the internal parameters of the machine learning model 202 becomeiteratively optimized. As those having ordinary skill in the art willappreciate, the training component 802 can implement any other suitabletraining batch sizes and/or any suitable number of training epochs.

FIG. 9 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 900 that can facilitate training of a domaintransformation machine learning model in accordance with one or moreembodiments described herein. In various cases, the learning-baseddomain transformation system 102 can facilitate the computer-implementedmethod 900.

In various embodiments, act 902 can include accessing, by a device(e.g., 112) operatively coupled to a processor, a machine learning model(e.g., 202) to be trained.

In various aspects, act 904 can include receiving, by the device (e.g.,112), a set of training images (e.g., 806) in a first medical scanningdomain (e.g., 106) and a respectively corresponding set of annotationimages (e.g., 808) in a second medical scanning domain (e.g., 206).

In various instances, act 906 can include determining, by the device(e.g., 802), whether all training images in the set have been used totraining the machine learning model. If so, the computer-implementedmethod 900 can proceed to act 920, where the computer-implemented method900 can end. If not, the computer-implemented method 900 can proceed toact 908.

In various cases, act 908 can include selecting, by the device (e.g.,802), a training image that has not yet been used to train the machinelearning model.

In various aspects, act 910 can include registering and/or aligning, bythe device (e.g., 802), the selected training image with a correspondingannotation image.

In various instances, act 912 can include identifying, by the device(e.g., 402) and via execution of a segmentation model (e.g., 404), aregion-of-interest in the selected training image and theregion-of-interest in the corresponding annotation image.

In various cases, act 914 can include cropping out, by the device (e.g.,402), portions of the selected training image that are outside of theregion-of-interest and portions of the corresponding annotation imagethat are outside of the region-of-interest. As those having ordinaryskill in the art will appreciate, the cropped version of the selectedtraining image and the cropped version of the corresponding annotationimage can have the same number and/or arrangement of pixels/voxels.

In various aspects, act 916 can include feeding, by the device (e.g.,114), the selected training image (e.g., the cropped version of theselected training image) to the machine learning model, thereby yieldinga predicted image. In various cases, the predicted image can representhow the selected training image would look in the second medicalscanning domain.

In various instances, act 918 can include updating, by the device (e.g.,802), internal parameters of the machine learning model, based on anerror between the predicted image and the corresponding annotation image(e.g., the cropped version of the corresponding annotation image). Invarious cases, the computer-implemented method 900 can proceed back toact 906.

As shown, acts 906-918 can iterate until all training images have beenused to train the machine learning model, or otherwise until any othersuitable training termination criterion is achieved (e.g., maximumnumber of epochs, minimum amount of error).

FIG. 10 illustrates a block diagram of an example, non-limiting system1000 including an execution component that can facilitate learning-baseddomain transformation for medical images in accordance with one or moreembodiments described herein. As shown, the system 1000 can, in somecases, comprise the same components as the system 800, and can furthercomprise an execution component 1002.

As mentioned above, it can be desired to apply a computerized diagnostictechnique (e.g., Agatston scoring) to the medical image 104. But suchapplication can be suboptimal since the computerized diagnostictechnique can only be accurately/reliably applied to medical images thatare captured/generated via the second medical scanning domain 206.Accordingly, in various embodiments, once the transformation component114 has produced the predicted image 204, the execution component 1002can electronically apply and/or execute the computerized diagnostictechnique on the predicted image 204. Note that the predicted image 204can contain the same medically-relevant substantive information as themedical image 104 (e.g., can depict the same anatomical structure as themedical image 104), but the predicted image 204 can appear to have beencaptured/generated via the second medical scanning domain 206 instead ofthe first medical scanning domain 106. Thus, the computerized diagnostictechnique can be reliably and/or accurately applied to the predictedimage 204, thereby yielding medically significant results for thepatient. In various cases, the execution component 1002 canelectronically transmit such results to any suitable computing device.

FIG. 11 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 1100 that can facilitate learning-baseddomain transformation for medical images in accordance with one or moreembodiments described herein. In various cases, the learning-baseddomain transformation system 102 can facilitate the computer-implementedmethod 1100.

In various embodiments, act 1102 can include accessing, by a device(e.g., 112) operatively coupled to a processor, a medical image (e.g.,104). In various cases, the medical image an depict an anatomicalstructure according to a first medical scanning domain (e.g., 106).

In various aspects, act 1104 can include generating, by the device(e.g., 114) and via execution of a machine learning model (e.g., 202), apredicted image (e.g., 204) based on the medical image. In variouscases, the predicted image an depict the anatomical structure accordingto a second medical scanning domain (e.g., 206) that is different fromthe first medical scanning domain.

Although not explicitly shown in FIG. 11 , the first medical scanningdomain can be a first energy level of a computed tomography (CT)scanning modality, and the second medical scanning domain can be asecond energy level of the CT scanning modality that is different fromthe first energy level.

Although not explicitly shown in FIG. 11 , the first medical scanningdomain can be a first contrast phase of a computed tomography (CT)scanning modality, and the second medical scanning domain can be asecond contrast phase of the CT scanning modality that is different fromthe first contrast phase.

Although not explicitly shown in FIG. 11 , the computer-implementedmethod 1100 can further comprise: identifying, by the device (e.g., 402)and via execution of a segmentation model (e.g., 404), aregion-of-interest (e.g., 406) in the medical image, wherein thegenerating the predicted image is based on executing the machinelearning model on the region-of-interest, and wherein the machinelearning model is not executed on a remainder of the medical image.

Although not explicitly shown in FIG. 11 , the computer-implementedmethod 1100 can further comprise: applying, by the device (e.g., 602),one or more denoising techniques to the medical image prior to executionof the machine learning model.

Although not explicitly shown in FIG. 11 , the computer-implementedmethod 1100 can further comprise: training, by the device (e.g., 802),the machine learning model on a training medical image (e.g., trainingimage 1), wherein the training medical image depicts one or moreanatomical structures according to the first medical scanning domain,and wherein the training medical image corresponds to a targetannotation image (e.g., annotation image 1) that depicts the one or moreanatomical structures according to the second medical scanning domain.In various cases, the computer-implemented method 1100 can furtherinclude: registering, by the device (e.g., 802), the training medicalimage with the target annotation image prior to the training.

Various embodiments described herein include a computerized tool thatcan access a medical image that has been generated/captured via a firstmedical scanning domain and that can transform and/or convert themedical image into a different medical scanning domain. As explainedabove, a medical scanning domain can be any suitable configurablesetting, configurable control, and/or configurable parameter of amedical imaging device/modality, where changing/adjusting the setting,control, and/or parameter can affect how a medical image visually looksonce captured/generated. As a non-limiting example, a medical scanningdomain can be an electrical energy level (e.g., capturing CT images at70 kVp can be considered as one medical scanning domain, while capturingCT images at 120 kVp can be considered as another medical scanningdomain). As another non-limiting example, a medical scanning domain canbe a contrast phase (e.g., capturing CT images during an early arterialphase can be considered as one medical scanning domain, while capturingCT images during a nephrogenic phase can be considered as anothermedical scanning domain). Accordingly, when various embodiments of thesubject innovation are implemented, a medical image can becaptured/generated in one medical scanning domain and can be transformedinto another medical scanning domain. This can be particularly usefulwhen the domain is an electrical energy level. For example, a CT imagecan be captured at a low electrical energy level (e.g., 70 kVp) so thatthe patient is not exposed to excessive radiation. Then, the CT imagecan be substantively transformed and/or converted to a high electricalenergy level (e.g., 120 kVp), such that diagnostic techniques (e.g.,Agatston scoring) that can only be applied to high-dose CT images can beutilized to analyze the CT image. Thus, various embodiments describedherein certainly constitute a concrete and tangible techniqueimprovement in the field of medical imaging.

Those having ordinary skill in the art will recognize that theherein-described teachings can be applied to various medical contexts(e.g., transforming CT images from a low electrical energy level to ahigh electrical energy level can be useful in calcium scoring and/orradiomics; transforming CT images from one contrast phase to anothercontrast phase can be useful in tissue classification, multi-phaselesion characterization, and/or the creation of virtual non-contrastimages).

Those having ordinary skill in the art will appreciate that the hereindisclosure describes non-limiting examples of various embodiments of theinvention. For ease of description and/or explanation, various portionsof the herein disclosure utilize the term “each” when discussing variousembodiments of the invention. Those having ordinary skill in the artwill appreciate that such usages of the term “each” are non-limitingexamples. In other words, when the herein disclosure provides adescription that is applied to “each” of some particular computerizedobject and/or component, it should be understood that this is anon-limiting example of various embodiments of the invention, and itshould be further understood that, in various other embodiments of theinvention, it can be the case that such description applies to fewerthan “each” of that particular computerized object.

To facilitate some of the above-described machine learning aspects ofvarious embodiments of the subject innovation, consider the followingdiscussion of artificial intelligence. Various embodiments of thepresent innovation herein can employ artificial intelligence (AI) tofacilitate automating one or more features of the present innovation.The components can employ various AI-based schemes for carrying outvarious embodiments/examples disclosed herein. In order to provide foror aid in the numerous determinations (e.g., determine, ascertain,infer, calculate, predict, prognose, estimate, derive, forecast, detect,compute) of the present innovation, components of the present innovationcan examine the entirety or a subset of the data to which it is grantedaccess and can provide for reasoning about or determine states of thesystem and/or environment from a set of observations as captured viaevents and/or data. Determinations can be employed to identify aspecific context or action, or can generate a probability distributionover states, for example. The determinations can be probabilistic; thatis, the computation of a probability distribution over states ofinterest based on a consideration of data and events. Determinations canalso refer to techniques employed for composing higher-level events froma set of events and/or data.

Such determinations can result in the construction of new events oractions from a set of observed events and/or stored event data, whetheror not the events are correlated in close temporal proximity, andwhether the events and data come from one or several event and datasources. Components disclosed herein can employ various classification(explicitly trained (e.g., via training data) as well as implicitlytrained (e.g., via observing behavior, preferences, historicalinformation, receiving extrinsic information, and so on)) schemes and/orsystems (e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines, and so on)in connection with performing automatic and/or determined action inconnection with the claimed subject matter. Thus, classification schemesand/or systems can be used to automatically learn and perform a numberof functions, actions, and/or determinations.

A classifier can map an input attribute vector, z = (z1, z2, z3, z4,zn), to a confidence that the input belongs to a class, as by f(z) =confidence(class). Such classification can employ a probabilistic and/orstatistical-based analysis (e.g., factoring into the analysis utilitiesand costs) to determinate an action to be automatically performed. Asupport vector machine (SVM) can be an example of a classifier that canbe employed. The SVM operates by finding a hyper-surface in the space ofpossible inputs, where the hyper-surface attempts to split thetriggering criteria from the non-triggering events. Intuitively, thismakes the classification correct for testing data that is near, but notidentical to training data. Other directed and undirected modelclassification approaches include, e.g., naive Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and/orprobabilistic classification models providing different patterns ofindependence, any of which can be employed. Classification as usedherein also is inclusive of statistical regression that is utilized todevelop models of priority.

In order to provide additional context for various embodiments describedherein, FIG. 12 and the following discussion are intended to provide abrief, general description of a suitable computing environment 1200 inwhich the various embodiments of the embodiment described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, Internet of Things (IoT)devices, distributed computing systems, as well as personal computers,hand-held computing devices, microprocessor-based or programmableconsumer electronics, and the like, each of which can be operativelycoupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD ROM), digitalversatile disk (DVD), Blu-ray disc (BD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, solid state drives or other solid statestorage devices, or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 12 , the example environment 1200 forimplementing various embodiments of the aspects described hereinincludes a computer 1202, the computer 1202 including a processing unit1204, a system memory 1206 and a system bus 1208. The system bus 1208couples system components including, but not limited to, the systemmemory 1206 to the processing unit 1204. The processing unit 1204 can beany of various commercially available processors. Dual microprocessorsand other multi processor architectures can also be employed as theprocessing unit 1204.

The system bus 1208 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1206includes ROM 1210 and RAM 1212. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1202, such as during startup. The RAM 1212 can also include a high-speedRAM such as static RAM for caching data.

The computer 1202 further includes an internal hard disk drive (HDD)1214 (e.g., EIDE, SATA), one or more external storage devices 1216(e.g., a magnetic floppy disk drive (FDD) 1216, a memory stick or flashdrive reader, a memory card reader, etc.) and a drive 1220, e.g., suchas a solid state drive, an optical disk drive, which can read or writefrom a disk 1222, such as a CD-ROM disc, a DVD, a BD, etc.Alternatively, where a solid state drive is involved, disk 1222 wouldnot be included, unless separate. While the internal HDD 1214 isillustrated as located within the computer 1202, the internal HDD 1214can also be configured for external use in a suitable chassis (notshown). Additionally, while not shown in environment 1200, a solid statedrive (SSD) could be used in addition to, or in place of, an HDD 1214.The HDD 1214, external storage device(s) 1216 and drive 1220 can beconnected to the system bus 1208 by an HDD interface 1224, an externalstorage interface 1226 and a drive interface 1228, respectively. Theinterface 1224 for external drive implementations can include at leastone or both of Universal Serial Bus (USB) and Institute of Electricaland Electronics Engineers (IEEE) 1394 interface technologies. Otherexternal drive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1202, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1212,including an operating system 1230, one or more application programs1232, other program modules 1234 and program data 1236. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1212. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1202 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1230, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 12 . In such an embodiment, operating system 1230 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1202.Furthermore, operating system 1230 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplications 1232. Runtime environments are consistent executionenvironments that allow applications 1232 to run on any operating systemthat includes the runtime environment. Similarly, operating system 1230can support containers, and applications 1232 can be in the form ofcontainers, which are lightweight, standalone, executable packages ofsoftware that include, e.g., code, runtime, system tools, systemlibraries and settings for an application.

Further, computer 1202 can be enable with a security module, such as atrusted processing module (TPM). For instance with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1202, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1202 throughone or more wired/wireless input devices, e.g., a keyboard 1238, a touchscreen 1240, and a pointing device, such as a mouse 1242. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1204 through an input deviceinterface 1244 that can be coupled to the system bus 1208, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH®interface, etc.

A monitor 1246 or other type of display device can be also connected tothe system bus 1208 via an interface, such as a video adapter 1248. Inaddition to the monitor 1246, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1202 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1250. The remotecomputer(s) 1250 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1202, although, for purposes of brevity, only a memory/storage device1252 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1254 and/orlarger networks, e.g., a wide area network (WAN) 1256. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1202 can beconnected to the local network 1254 through a wired and/or wirelesscommunication network interface or adapter 1258. The adapter 1258 canfacilitate wired or wireless communication to the LAN 1254, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1258 in a wireless mode.

When used in a WAN networking environment, the computer 1202 can includea modem 1260 or can be connected to a communications server on the WAN1256 via other means for establishing communications over the WAN 1256,such as by way of the Internet. The modem 1260, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1208 via the input device interface 1244. In a networkedenvironment, program modules depicted relative to the computer 1202 orportions thereof, can be stored in the remote memory/storage device1252. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1202 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1216 asdescribed above, such as but not limited to a network virtual machineproviding one or more aspects of storage or processing of information.Generally, a connection between the computer 1202 and a cloud storagesystem can be established over a LAN 1254 or WAN 1256 e.g., by theadapter 1258 or modem 1260, respectively. Upon connecting the computer1202 to an associated cloud storage system, the external storageinterface 1226 can, with the aid of the adapter 1258 and/or modem 1260,manage storage provided by the cloud storage system as it would othertypes of external storage. For instance, the external storage interface1226 can be configured to provide access to cloud storage sources as ifthose sources were physically connected to the computer 1202.

The computer 1202 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

FIG. 13 is a schematic block diagram of a sample computing environment1300 with which the disclosed subject matter can interact. The samplecomputing environment 1300 includes one or more client(s) 1310. Theclient(s) 1310 can be hardware and/or software (e.g., threads,processes, computing devices). The sample computing environment 1300also includes one or more server(s) 1330. The server(s) 1330 can also behardware and/or software (e.g., threads, processes, computing devices).The servers 1330 can house threads to perform transformations byemploying one or more embodiments as described herein, for example. Onepossible communication between a client 1310 and a server 1330 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The sample computing environment 1300 includes acommunication framework 1350 that can be employed to facilitatecommunications between the client(s) 1310 and the server(s) 1330. Theclient(s) 1310 are operably connected to one or more client datastore(s) 1320 that can be employed to store information local to theclient(s) 1310. Similarly, the server(s) 1330 are operably connected toone or more server data store(s) 1340 that can be employed to storeinformation local to the servers 1330.

The present invention may be a system, a method, an apparatus and/or acomputer program product at any possible technical detail level ofintegration. The computer program product can include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device. The computer readable storage medium canbe, for example, but is not limited to, an electronic storage device, amagnetic storage device, an optical storage device, an electromagneticstorage device, a semiconductor storage device, or any suitablecombination of the foregoing. A non-exhaustive list of more specificexamples of the computer readable storage medium can also include thefollowing: a portable computer diskette, a hard disk, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of the present invention can beassembler instructions, instruction-set-architecture (ISA) instructions,machine instructions, machine dependent instructions, microcode,firmware instructions, state-setting data, configuration data forintegrated circuitry, or either source code or object code written inany combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions can execute entirely on the user’s computer, partly on theuser’s computer, as a stand-alone software package, partly on the user’scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer can beconnected to the user’s computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection can be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) can execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments in which tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a processor that executescomputer-executable components stored in a computer-readable memory, thecomputer-executable components comprising: a receiver component thataccesses a medical image, wherein the medical image depicts ananatomical structure according to a first medical scanning domain; and atransformation component that generates, via execution of a machinelearning model, a predicted image based on the medical image, whereinthe predicted image depicts the anatomical structure according to asecond medical scanning domain that is different from the first medicalscanning domain.
 2. The system of claim 1, wherein the first medicalscanning domain is a first energy level of a computed tomography (CT)scanning modality, and wherein the second medical scanning domain is asecond energy level of the CT scanning modality that is different fromthe first energy level.
 3. The system of claim 1, wherein the firstmedical scanning domain is a first contrast phase of a computedtomography (CT) scanning modality, and wherein the second medicalscanning domain is a second contrast phase of the CT scanning modalitythat is different from the first contrast phase.
 4. The system of claim1, wherein the computer-executable components further comprise: asegmentation component that identifies, via execution of a segmentationmodel, a region-of-interest in the medical image, wherein thetransformation component generates the predicted image by executing themachine learning model on the region-of-interest, and wherein thetransformation component does not execute the machine learning model ona remainder of the medical image.
 5. The system of claim 1, wherein thecomputer-executable components further comprise: a denoise componentthat applies one or more denoising techniques to the medical image priorto execution of the machine learning model.
 6. The system of claim 1,wherein the machine learning model is a deep learning regression model.7. The system of claim 1, wherein the computer-executable componentsfurther comprise: a training component that trains the machine learningmodel on a training medical image, wherein the training medical imagedepicts one or more anatomical structures according to the first medicalscanning domain, and wherein the training medical image corresponds to atarget annotation image that depicts the one or more anatomicalstructures according to the second medical scanning domain.
 8. Thesystem of claim 7, wherein the training component registers the trainingmedical image with the target annotation image prior to training themachine learning model.
 9. A computer-implemented method, comprising:accessing, by a device operatively coupled to a processor, a medicalimage, wherein the medical image depicts an anatomical structureaccording to a first medical scanning domain; and generating, by thedevice and via execution of a machine learning model, a predicted imagebased on the medical image, wherein the predicted image depicts theanatomical structure according to a second medical scanning domain thatis different from the first medical scanning domain.
 10. Thecomputer-implemented method of claim 9, wherein the first medicalscanning domain is a first energy level of a computed tomography (CT)scanning modality, and wherein the second medical scanning domain is asecond energy level of the CT scanning modality that is different fromthe first energy level.
 11. The computer-implemented method of claim 9,wherein the first medical scanning domain is a first contrast phase of acomputed tomography (CT) scanning modality, and wherein the secondmedical scanning domain is a second contrast phase of the CT scanningmodality that is different from the first contrast phase.
 12. Thecomputer-implemented method of claim 9, further comprising: identifying,by the device and via execution of a segmentation model, aregion-of-interest in the medical image, wherein the generating thepredicted image is based on executing the machine learning model on theregion-of-interest, and wherein the machine learning model is notexecuted on a remainder of the medical image.
 13. Thecomputer-implemented method of claim 9, further comprising: applying, bythe device, one or more denoising techniques to the medical image priorto execution of the machine learning model.
 14. The computer-implementedmethod of claim 9, wherein the machine learning model is a deep learningregression model.
 15. The computer-implemented method of claim 9,further comprising: training, by the device, the machine learning modelon a training medical image, wherein the training medical image depictsone or more anatomical structures according to the first medicalscanning domain, and wherein the training medical image corresponds to atarget annotation image that depicts the one or more anatomicalstructures according to the second medical scanning domain.
 16. Thecomputer-implemented method of claim 15, further comprising:registering, by the device, the training medical image with the targetannotation image prior to the training.
 17. A computer program productfor facilitating learning-based domain transformation for medicalimages, the computer program product comprising a computer-readablememory having program instructions embodied therewith, the programinstructions executable by a processor to cause the processor to: accessa medical image, wherein the medical image depicts an anatomicalstructure according to a first medical scanning domain; and generate,via execution of a machine learning model, a predicted image based onthe medical image, wherein the predicted image depicts the anatomicalstructure according to a second medical scanning domain that isdifferent from the first medical scanning domain.
 18. The computerprogram product of claim 17, wherein the first medical scanning domainis a first energy level of a computed tomography (CT) scanning modality,and wherein the second medical scanning domain is a second energy levelof the CT scanning modality that is different from the first energylevel.
 19. The computer program product of claim 17, wherein the firstmedical scanning domain is a first contrast phase of a computedtomography (CT) scanning modality, and wherein the second medicalscanning domain is a second contrast phase of the CT scanning modalitythat is different from the first contrast phase.
 20. The computerprogram product of claim 17, wherein the program instructions arefurther executable to cause the processor to: identify, via execution ofa segmentation model, a region-of-interest in the medical image, whereinthe processor generates the predicted image by executing the machinelearning model on the region-of-interest, and wherein the machinelearning model is not executed on a remainder of the medical image.